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Update YOLO11 Actions and Docs (#16596)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
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Ultralytics Assistant 2024-10-01 16:58:12 +02:00 • committed by GitHub
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docs/en/datasets/explorer/api.md
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@ -36,7 +36,7 @@ pip install ultralytics[explorer]
from ultralytics import Explorer
# Create an Explorer object
explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
# Create embeddings for your dataset
explorer.create_embeddings_table()
@ -75,7 +75,7 @@ You get a pandas dataframe with the `limit` number of most similar data points t
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
@ -95,7 +95,7 @@ You get a pandas dataframe with the `limit` number of most similar data points t
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(idx=1, limit=10)
@ -118,7 +118,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
plt = exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
@ -131,7 +131,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
plt = exp.plot_similar(idx=1, limit=10)
@ -150,7 +150,7 @@ Note: This works using LLMs under the hood so the results are probabilistic and
from ultralytics.data.explorer import plot_query_result
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
@ -171,7 +171,7 @@ You can run SQL queries on your dataset using the `sql_query` method. This metho
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
@ -188,7 +188,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp = Explorer(data="coco128.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
# plot the SQL Query
@ -235,7 +235,7 @@ Here are some examples of what you can do with the table:
```python
from ultralytics import Explorer
exp = Explorer(model="yolov8n.pt")
exp = Explorer(model="yolo11n.pt")
exp.create_embeddings_table()
table = exp.table
@ -359,7 +359,7 @@ You can use the Ultralytics Explorer API to perform similarity searches by creat
from ultralytics import Explorer
# Create an Explorer object
explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
explorer.create_embeddings_table()
# Search for similar images to a given image
@ -381,7 +381,7 @@ The Ask AI feature allows users to filter datasets using natural language querie
from ultralytics import Explorer
# Create an Explorer object
explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
explorer = Explorer(data="coco128.yaml", model="yolo11n.pt")
explorer.create_embeddings_table()
# Query with natural language